//
// Created by pulsarv on 19-12-8.
//



#ifndef MOBILESEARCH_COMMON_HPP
#define MOBILESEARCH_COMMON_HPP

/**
 * @brief a header file with common samples functionality
 * @file common.hpp
 */


#include <string>
#include <map>
#include <vector>
#include <list>
#include <limits>
#include <functional>
#include <fstream>
#include <iomanip>
#include <utility>
#include <algorithm>
#include <random>

#include <ie_core.hpp>
#include <ie_plugin_config.hpp>
#include <cpp/ie_infer_request.hpp>
#include <ie_blob.h>

#ifndef UNUSED
#ifdef WIN32
#define UNUSED
#else
#define UNUSED  __attribute__((unused))
#endif
#endif

/**
 * @brief This class represents a console error listener.
 *
 */
class ConsoleErrorListener : public InferenceEngine::IErrorListener {
    /**
     * @brief The plugin calls this method with a null terminated error message (in case of error)
     * @param msg Error message
     */
    void onError(const char *msg) noexcept override {
        std::clog << "Device message: " << msg << std::endl;
    }
};

/**
 * @brief Trims from both ends (in place)
 * @param s - string to trim
 * @return trimmed string
 */
inline std::string &trim(std::string &s) {
    s.erase(s.begin(), std::find_if(s.begin(), s.end(), std::not1(std::ptr_fun<int, int>(std::isspace))));
    s.erase(std::find_if(s.rbegin(), s.rend(), std::not1(std::ptr_fun<int, int>(std::isspace))).base(), s.end());
    return s;
}

/**
 * @brief Gets filename without extension
 * @param filepath - full file name
 * @return filename without extension
 */
static UNUSED std::string fileNameNoExt(const std::string &filepath) {
    auto pos = filepath.rfind('.');
    if (pos == std::string::npos) return filepath;
    return filepath.substr(0, pos);
}

/**
* @brief Get extension from filename
* @param filename - name of the file which extension should be extracted
* @return string with extracted file extension
*/
inline std::string fileExt(const std::string &filename) {
    auto pos = filename.rfind('.');
    if (pos == std::string::npos) return "";
    return filename.substr(pos + 1);
}

static UNUSED std::ostream &operator<<(std::ostream &os, const InferenceEngine::Version *version) {
    os << "\n\tAPI version ............ ";
    if (nullptr == version) {
        os << "UNKNOWN";
    } else {
        os << version->apiVersion.major << "." << version->apiVersion.minor;
        if (nullptr != version->buildNumber) {
            os << "\n\t" << "Build .................. " << version->buildNumber;
        }
        if (nullptr != version->description) {
            os << "\n\t" << "Description ....... " << version->description;
        }
    }
    return os;
}

inline std::ostream &operator<<(std::ostream &os, const InferenceEngine::Version &version) {
    os << "\t" << version.description << " version ......... ";
    os << version.apiVersion.major << "." << version.apiVersion.minor;

    os << "\n\tBuild ........... ";
    os << version.buildNumber;

    return os;
}

inline std::ostream &operator<<(std::ostream &os, const std::map<std::string, InferenceEngine::Version> &versions) {
    for (auto &&version : versions) {
        os << "\t" << version.first << std::endl;
        os << version.second << std::endl;
    }

    return os;
}

static UNUSED std::vector<std::vector<size_t>> blobToImageOutputArray(InferenceEngine::TBlob<float>::Ptr output,
                                                                      size_t *pWidth, size_t *pHeight,
                                                                      size_t *pChannels) {
    std::vector<std::vector<size_t>> outArray;
    size_t W = 0, C = 0, H = 0;

    auto outputDims = output->getTensorDesc().getDims();
    if (outputDims.size() == 3) {
        C = outputDims.at(0);
        H = outputDims.at(1);
        W = outputDims.at(2);
    } else if (outputDims.size() == 4) {
        C = outputDims.at(1);
        H = outputDims.at(2);
        W = outputDims.at(3);
    } else if (outputDims.size() == 5) {
        C = outputDims.at(1);
        H = outputDims.at(3);
        W = outputDims.at(4);
    } else {
        THROW_IE_EXCEPTION << "Output blob has unsupported layout " << output->getTensorDesc().getLayout();
    }

    // Get classes
    const float *outData = output->data();
    for (unsigned h = 0; h < H; h++) {
        std::vector<size_t> row;
        for (unsigned w = 0; w < W; w++) {
            float max_value = outData[h * W + w];
            size_t index = 0;
            for (size_t c = 1; c < C; c++) {
                size_t dataIndex = c * H * W + h * W + w;
                if (outData[dataIndex] > max_value) {
                    index = c;
                    max_value = outData[dataIndex];
                }
            }
            row.push_back(index);
        }
        outArray.push_back(row);
    }

    if (pWidth != nullptr) *pWidth = W;
    if (pHeight != nullptr) *pHeight = H;
    if (pChannels != nullptr) *pChannels = C;

    return outArray;
}

/**
 * @class Color
 * @brief A Color class stores channels of a given color
 */
class Color {
private:
    unsigned char _r;
    unsigned char _g;
    unsigned char _b;

public:
    /**
     * A default constructor.
     * @param r - value for red channel
     * @param g - value for green channel
     * @param b - value for blue channel
     */
    Color(unsigned char r,
          unsigned char g,
          unsigned char b) : _r(r), _g(g), _b(b) {}

    inline unsigned char red() {
        return _r;
    }

    inline unsigned char blue() {
        return _b;
    }

    inline unsigned char green() {
        return _g;
    }
};

// TODO : keep only one version of writeOutputBMP

/**
 * @brief Writes output data to image
 * @param name - image name
 * @param data - output data
 * @param classesNum - the number of classes
 * @return false if error else true
 */
static UNUSED void writeOutputBmp(std::vector<std::vector<size_t>> data, size_t classesNum, std::ostream &outFile) {
    unsigned int seed = (unsigned int) time(NULL);
    // Known colors for training classes from Cityscape dataset
    static std::vector<Color> colors = {
            {128, 64,  128},
            {232, 35,  244},
            {70,  70,  70},
            {156, 102, 102},
            {153, 153, 190},
            {153, 153, 153},
            {30,  170, 250},
            {0,   220, 220},
            {35,  142, 107},
            {152, 251, 152},
            {180, 130, 70},
            {60,  20,  220},
            {0,   0,   255},
            {142, 0,   0},
            {70,  0,   0},
            {100, 60,  0},
            {90,  0,   0},
            {230, 0,   0},
            {32,  11,  119},
            {0,   74,  111},
            {81,  0,   81}
    };

    while (classesNum > colors.size()) {
        static std::mt19937 rng(seed);
        std::uniform_int_distribution<int> dist(0, 255);
        Color color(dist(rng), dist(rng), dist(rng));
        colors.push_back(color);
    }

    unsigned char file[14] = {
            'B', 'M',           // magic
            0, 0, 0, 0,         // size in bytes
            0, 0,               // app data
            0, 0,               // app data
            40 + 14, 0, 0, 0      // start of data offset
    };
    unsigned char info[40] = {
            40, 0, 0, 0,        // info hd size
            0, 0, 0, 0,         // width
            0, 0, 0, 0,         // height
            1, 0,               // number color planes
            24, 0,              // bits per pixel
            0, 0, 0, 0,         // compression is none
            0, 0, 0, 0,         // image bits size
            0x13, 0x0B, 0, 0,   // horz resolution in pixel / m
            0x13, 0x0B, 0, 0,   // vert resolution (0x03C3 = 96 dpi, 0x0B13 = 72 dpi)
            0, 0, 0, 0,         // #colors in palette
            0, 0, 0, 0,         // #important colors
    };

    auto height = data.size();
    auto width = data.at(0).size();

    if (height > (size_t) std::numeric_limits<int32_t>::max || width > (size_t) std::numeric_limits<int32_t>::max) {
        THROW_IE_EXCEPTION << "File size is too big: " << height << " X " << width;
    }

    int padSize = static_cast<int>(4 - (width * 3) % 4) % 4;
    int sizeData = static_cast<int>(width * height * 3 + height * padSize);
    int sizeAll = sizeData + sizeof(file) + sizeof(info);

    file[2] = (unsigned char) (sizeAll);
    file[3] = (unsigned char) (sizeAll >> 8);
    file[4] = (unsigned char) (sizeAll >> 16);
    file[5] = (unsigned char) (sizeAll >> 24);

    info[4] = (unsigned char) (width);
    info[5] = (unsigned char) (width >> 8);
    info[6] = (unsigned char) (width >> 16);
    info[7] = (unsigned char) (width >> 24);

    int32_t negativeHeight = -(int32_t) height;
    info[8] = (unsigned char) (negativeHeight);
    info[9] = (unsigned char) (negativeHeight >> 8);
    info[10] = (unsigned char) (negativeHeight >> 16);
    info[11] = (unsigned char) (negativeHeight >> 24);

    info[20] = (unsigned char) (sizeData);
    info[21] = (unsigned char) (sizeData >> 8);
    info[22] = (unsigned char) (sizeData >> 16);
    info[23] = (unsigned char) (sizeData >> 24);

    outFile.write(reinterpret_cast<char *>(file), sizeof(file));
    outFile.write(reinterpret_cast<char *>(info), sizeof(info));

    unsigned char pad[3] = {0, 0, 0};

    for (size_t y = 0; y < height; y++) {
        for (size_t x = 0; x < width; x++) {
            unsigned char pixel[3];
            size_t index = data.at(y).at(x);
            pixel[0] = colors.at(index).red();
            pixel[1] = colors.at(index).green();
            pixel[2] = colors.at(index).blue();
            outFile.write(reinterpret_cast<char *>(pixel), 3);
        }
        outFile.write(reinterpret_cast<char *>(pad), padSize);
    }
}

/**
* @brief Writes output data to BMP image
* @param name - image name
* @param data - output data
* @param height - height of the target image
* @param width - width of the target image
* @return false if error else true
*/
static UNUSED bool writeOutputBmp(std::string name, unsigned char *data, size_t height, size_t width) {
    std::ofstream outFile;
    outFile.open(name, std::ofstream::binary);
    if (!outFile.is_open()) {
        return false;
    }

    unsigned char file[14] = {
            'B', 'M',           // magic
            0, 0, 0, 0,         // size in bytes
            0, 0,               // app data
            0, 0,               // app data
            40 + 14, 0, 0, 0      // start of data offset
    };
    unsigned char info[40] = {
            40, 0, 0, 0,        // info hd size
            0, 0, 0, 0,         // width
            0, 0, 0, 0,         // height
            1, 0,               // number color planes
            24, 0,              // bits per pixel
            0, 0, 0, 0,         // compression is none
            0, 0, 0, 0,         // image bits size
            0x13, 0x0B, 0, 0,   // horz resolution in pixel / m
            0x13, 0x0B, 0, 0,   // vert resolution (0x03C3 = 96 dpi, 0x0B13 = 72 dpi)
            0, 0, 0, 0,         // #colors in palette
            0, 0, 0, 0,         // #important colors
    };

    if (height > (size_t) std::numeric_limits<int32_t>::max || width > (size_t) std::numeric_limits<int32_t>::max) {
        THROW_IE_EXCEPTION << "File size is too big: " << height << " X " << width;
    }

    int padSize = static_cast<int>(4 - (width * 3) % 4) % 4;
    int sizeData = static_cast<int>(width * height * 3 + height * padSize);
    int sizeAll = sizeData + sizeof(file) + sizeof(info);

    file[2] = (unsigned char) (sizeAll);
    file[3] = (unsigned char) (sizeAll >> 8);
    file[4] = (unsigned char) (sizeAll >> 16);
    file[5] = (unsigned char) (sizeAll >> 24);

    info[4] = (unsigned char) (width);
    info[5] = (unsigned char) (width >> 8);
    info[6] = (unsigned char) (width >> 16);
    info[7] = (unsigned char) (width >> 24);

    int32_t negativeHeight = -(int32_t) height;
    info[8] = (unsigned char) (negativeHeight);
    info[9] = (unsigned char) (negativeHeight >> 8);
    info[10] = (unsigned char) (negativeHeight >> 16);
    info[11] = (unsigned char) (negativeHeight >> 24);

    info[20] = (unsigned char) (sizeData);
    info[21] = (unsigned char) (sizeData >> 8);
    info[22] = (unsigned char) (sizeData >> 16);
    info[23] = (unsigned char) (sizeData >> 24);

    outFile.write(reinterpret_cast<char *>(file), sizeof(file));
    outFile.write(reinterpret_cast<char *>(info), sizeof(info));

    unsigned char pad[3] = {0, 0, 0};

    for (size_t y = 0; y < height; y++) {
        for (size_t x = 0; x < width; x++) {
            unsigned char pixel[3];
            pixel[0] = data[y * width * 3 + x * 3];
            pixel[1] = data[y * width * 3 + x * 3 + 1];
            pixel[2] = data[y * width * 3 + x * 3 + 2];

            outFile.write(reinterpret_cast<char *>(pixel), 3);
        }
        outFile.write(reinterpret_cast<char *>(pad), padSize);
    }
    return true;
}


/**
* @brief Adds colored rectangles to the image
* @param data - data where rectangles are put
* @param height - height of the rectangle
* @param width - width of the rectangle
* @param rectangles - vector points for the rectangle, should be 4x compared to num classes
* @param classes - vector of classes
* @param thickness - thickness of a line (in pixels) to be used for bounding boxes
*/
static UNUSED void
addRectangles(unsigned char *data, size_t height, size_t width, std::vector<int> rectangles, std::vector<int> classes,
              int thickness = 1) {
    std::vector<Color> colors = {  // colors to be used for bounding boxes
            {128, 64,  128},
            {232, 35,  244},
            {70,  70,  70},
            {156, 102, 102},
            {153, 153, 190},
            {153, 153, 153},
            {30,  170, 250},
            {0,   220, 220},
            {35,  142, 107},
            {152, 251, 152},
            {180, 130, 70},
            {60,  20,  220},
            {0,   0,   255},
            {142, 0,   0},
            {70,  0,   0},
            {100, 60,  0},
            {90,  0,   0},
            {230, 0,   0},
            {32,  11,  119},
            {0,   74,  111},
            {81,  0,   81}
    };
    if (rectangles.size() % 4 != 0 || rectangles.size() / 4 != classes.size()) {
        return;
    }

    for (size_t i = 0; i < classes.size(); i++) {
        int x = rectangles.at(i * 4);
        int y = rectangles.at(i * 4 + 1);
        int w = rectangles.at(i * 4 + 2);
        int h = rectangles.at(i * 4 + 3);

        int cls = classes.at(i) % colors.size();  // color of a bounding box line

        if (x < 0) x = 0;
        if (y < 0) y = 0;
        if (w < 0) w = 0;
        if (h < 0) h = 0;

        if (static_cast<std::size_t>(x) >= width) {
            x = width - 1;
            w = 0;
            thickness = 1;
        }
        if (static_cast<std::size_t>(y) >= height) {
            y = height - 1;
            h = 0;
            thickness = 1;
        }

        if (static_cast<std::size_t>(x + w) >= width) { w = width - x - 1; }
        if (static_cast<std::size_t>(y + h) >= height) { h = height - y - 1; }

        thickness = std::min(std::min(thickness, w / 2 + 1), h / 2 + 1);

        size_t shift_first;
        size_t shift_second;
        for (int t = 0; t < thickness; t++) {
            shift_first = (y + t) * width * 3;
            shift_second = (y + h - t) * width * 3;
            for (int ii = x; ii < x + w + 1; ii++) {
                data[shift_first + ii * 3] = colors.at(cls).red();
                data[shift_first + ii * 3 + 1] = colors.at(cls).green();
                data[shift_first + ii * 3 + 2] = colors.at(cls).blue();
                data[shift_second + ii * 3] = colors.at(cls).red();
                data[shift_second + ii * 3 + 1] = colors.at(cls).green();
                data[shift_second + ii * 3 + 2] = colors.at(cls).blue();
            }
        }

        for (int t = 0; t < thickness; t++) {
            shift_first = (x + t) * 3;
            shift_second = (x + w - t) * 3;
            for (int ii = y; ii < y + h + 1; ii++) {
                data[shift_first + ii * width * 3] = colors.at(cls).red();
                data[shift_first + ii * width * 3 + 1] = colors.at(cls).green();
                data[shift_first + ii * width * 3 + 2] = colors.at(cls).blue();
                data[shift_second + ii * width * 3] = colors.at(cls).red();
                data[shift_second + ii * width * 3 + 1] = colors.at(cls).green();
                data[shift_second + ii * width * 3 + 2] = colors.at(cls).blue();
            }
        }
    }
}


/**
 * Write output data to image
 * \param name - image name
 * \param data - output data
 * \param classesNum - the number of classes
 * \return false if error else true
 */

static UNUSED bool writeOutputBmp(unsigned char *data, size_t height, size_t width, std::ostream &outFile) {
    unsigned char file[14] = {
            'B', 'M',           // magic
            0, 0, 0, 0,         // size in bytes
            0, 0,               // app data
            0, 0,               // app data
            40 + 14, 0, 0, 0      // start of data offset
    };
    unsigned char info[40] = {
            40, 0, 0, 0,        // info hd size
            0, 0, 0, 0,         // width
            0, 0, 0, 0,         // height
            1, 0,               // number color planes
            24, 0,              // bits per pixel
            0, 0, 0, 0,         // compression is none
            0, 0, 0, 0,         // image bits size
            0x13, 0x0B, 0, 0,   // horz resolution in pixel / m
            0x13, 0x0B, 0, 0,   // vert resolution (0x03C3 = 96 dpi, 0x0B13 = 72 dpi)
            0, 0, 0, 0,         // #colors in palette
            0, 0, 0, 0,         // #important colors
    };

    if (height > (size_t) std::numeric_limits<int32_t>::max || width > (size_t) std::numeric_limits<int32_t>::max) {
        THROW_IE_EXCEPTION << "File size is too big: " << height << " X " << width;
    }

    int padSize = static_cast<int>(4 - (width * 3) % 4) % 4;
    int sizeData = static_cast<int>(width * height * 3 + height * padSize);
    int sizeAll = sizeData + sizeof(file) + sizeof(info);

    file[2] = (unsigned char) (sizeAll);
    file[3] = (unsigned char) (sizeAll >> 8);
    file[4] = (unsigned char) (sizeAll >> 16);
    file[5] = (unsigned char) (sizeAll >> 24);

    info[4] = (unsigned char) (width);
    info[5] = (unsigned char) (width >> 8);
    info[6] = (unsigned char) (width >> 16);
    info[7] = (unsigned char) (width >> 24);

    int32_t negativeHeight = -(int32_t) height;
    info[8] = (unsigned char) (negativeHeight);
    info[9] = (unsigned char) (negativeHeight >> 8);
    info[10] = (unsigned char) (negativeHeight >> 16);
    info[11] = (unsigned char) (negativeHeight >> 24);

    info[20] = (unsigned char) (sizeData);
    info[21] = (unsigned char) (sizeData >> 8);
    info[22] = (unsigned char) (sizeData >> 16);
    info[23] = (unsigned char) (sizeData >> 24);

    outFile.write(reinterpret_cast<char *>(file), sizeof(file));
    outFile.write(reinterpret_cast<char *>(info), sizeof(info));

    unsigned char pad[3] = {0, 0, 0};

    for (size_t y = 0; y < height; y++) {
        for (size_t x = 0; x < width; x++) {
            unsigned char pixel[3];
            pixel[0] = data[y * width * 3 + x * 3];
            pixel[1] = data[y * width * 3 + x * 3 + 1];
            pixel[2] = data[y * width * 3 + x * 3 + 2];
            outFile.write(reinterpret_cast<char *>(pixel), 3);
        }
        outFile.write(reinterpret_cast<char *>(pad), padSize);
    }

    return true;
}

static std::vector<std::pair<std::string, InferenceEngine::InferenceEngineProfileInfo>>
perfCountersSorted(std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> perfMap) {
    using perfItem = std::pair<std::string, InferenceEngine::InferenceEngineProfileInfo>;
    std::vector<perfItem> sorted;
    for (auto &kvp : perfMap) sorted.push_back(kvp);

    std::stable_sort(sorted.begin(), sorted.end(),
                     [](const perfItem &l, const perfItem &r) {
                         return l.second.execution_index < r.second.execution_index;
                     });

    return sorted;
}

static UNUSED void
printPerformanceCounts(const std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> &performanceMap,
                       std::ostream &stream, std::string deviceName,
                       bool bshowHeader = true) {
    long long totalTime = 0;
    // Print performance counts
    if (bshowHeader) {
        stream << std::endl << "performance counts:" << std::endl << std::endl;
    }

    auto performanceMapSorted = perfCountersSorted(performanceMap);

    for (const auto &it : performanceMapSorted) {
        std::string toPrint(it.first);
        const int maxLayerName = 30;

        if (it.first.length() >= maxLayerName) {
            toPrint = it.first.substr(0, maxLayerName - 4);
            toPrint += "...";
        }


        stream << std::setw(maxLayerName) << std::left << toPrint;
        switch (it.second.status) {
            case InferenceEngine::InferenceEngineProfileInfo::EXECUTED:
                stream << std::setw(15) << std::left << "EXECUTED";
                break;
            case InferenceEngine::InferenceEngineProfileInfo::NOT_RUN:
                stream << std::setw(15) << std::left << "NOT_RUN";
                break;
            case InferenceEngine::InferenceEngineProfileInfo::OPTIMIZED_OUT:
                stream << std::setw(15) << std::left << "OPTIMIZED_OUT";
                break;
        }
        stream << std::setw(30) << std::left << "layerType: " + std::string(it.second.layer_type) + " ";
        stream << std::setw(20) << std::left << "realTime: " + std::to_string(it.second.realTime_uSec);
        stream << std::setw(20) << std::left << "cpu: " + std::to_string(it.second.cpu_uSec);
        stream << " execType: " << it.second.exec_type << std::endl;
        if (it.second.realTime_uSec > 0) {
            totalTime += it.second.realTime_uSec;
        }
    }
    stream << std::setw(20) << std::left << "Total time: " + std::to_string(totalTime) << " microseconds" << std::endl;
    std::cout << std::endl;
    std::cout << "Full device name: " << deviceName << std::endl;
    std::cout << std::endl;
}

static UNUSED void
printPerformanceCounts(InferenceEngine::InferRequest request, std::ostream &stream, std::string deviceName,
                       bool bshowHeader = true) {
    auto performanceMap = request.GetPerformanceCounts();
    printPerformanceCounts(performanceMap, stream, deviceName, bshowHeader);
}

inline std::map<std::string, std::string>
getMapFullDevicesNames(InferenceEngine::Core &ie, std::vector<std::string> devices) {
    std::map<std::string, std::string> devicesMap;
    InferenceEngine::Parameter p;
    for (std::string &deviceName : devices) {
        if (deviceName != "") {
            try {
                p = ie.GetMetric(deviceName, METRIC_KEY(FULL_DEVICE_NAME));
                devicesMap.insert(std::pair<std::string, std::string>(deviceName, p.as<std::string>()));
            }
            catch (InferenceEngine::details::InferenceEngineException &) {
            }
        }
    }
    return devicesMap;
}

inline std::string getFullDeviceName(std::map<std::string, std::string> &devicesMap, std::string device) {
    std::map<std::string, std::string>::iterator it = devicesMap.find(device);
    if (it != devicesMap.end()) {
        return it->second;
    } else {
        return "";
    }
}

inline std::string getFullDeviceName(InferenceEngine::Core &ie, std::string device) {
    InferenceEngine::Parameter p;
    try {
        p = ie.GetMetric(device, METRIC_KEY(FULL_DEVICE_NAME));
        return p.as<std::string>();
    }
    catch (InferenceEngine::details::InferenceEngineException &) {
        return "";
    }
}

/**
 * @brief This class represents an object that is found by an object detection net
 */
class DetectedObject {
public:
    int objectType;
    float xmin, xmax, ymin, ymax, prob;
    bool difficult;

    DetectedObject(int _objectType, float _xmin, float _ymin, float _xmax, float _ymax, float _prob,
                   bool _difficult = false)
            : objectType(_objectType), xmin(_xmin), xmax(_xmax), ymin(_ymin), ymax(_ymax), prob(_prob),
              difficult(_difficult) {
    }

    DetectedObject(const DetectedObject &other) = default;

    static float ioU(const DetectedObject &detectedObject1_, const DetectedObject &detectedObject2_) {
        // Add small space to eliminate empty squares
        float epsilon = 0;  // 1e-5f;

        DetectedObject detectedObject1(detectedObject1_.objectType,
                                       (detectedObject1_.xmin - epsilon),
                                       (detectedObject1_.ymin - epsilon),
                                       (detectedObject1_.xmax - epsilon),
                                       (detectedObject1_.ymax - epsilon), detectedObject1_.prob);
        DetectedObject detectedObject2(detectedObject2_.objectType,
                                       (detectedObject2_.xmin + epsilon),
                                       (detectedObject2_.ymin + epsilon),
                                       (detectedObject2_.xmax),
                                       (detectedObject2_.ymax), detectedObject2_.prob);

        if (detectedObject1.objectType != detectedObject2.objectType) {
            // objects are different, so the result is 0
            return 0.0f;
        }

        if (detectedObject1.xmax < detectedObject1.xmin) return 0.0;
        if (detectedObject1.ymax < detectedObject1.ymin) return 0.0;
        if (detectedObject2.xmax < detectedObject2.xmin) return 0.0;
        if (detectedObject2.ymax < detectedObject2.ymin) return 0.0;


        float xmin = (std::max)(detectedObject1.xmin, detectedObject2.xmin);
        float ymin = (std::max)(detectedObject1.ymin, detectedObject2.ymin);
        float xmax = (std::min)(detectedObject1.xmax, detectedObject2.xmax);
        float ymax = (std::min)(detectedObject1.ymax, detectedObject2.ymax);

        // Caffe adds 1 to every length if the box isn't normalized. So do we...
        float addendum;
        if (xmax > 1 || ymax > 1)
            addendum = 1;
        else
            addendum = 0;

        // intersection
        float intr;
        if ((xmax >= xmin) && (ymax >= ymin)) {
            intr = (addendum + xmax - xmin) * (addendum + ymax - ymin);
        } else {
            intr = 0.0f;
        }

        // union
        float square1 = (addendum + detectedObject1.xmax - detectedObject1.xmin) *
                        (addendum + detectedObject1.ymax - detectedObject1.ymin);
        float square2 = (addendum + detectedObject2.xmax - detectedObject2.xmin) *
                        (addendum + detectedObject2.ymax - detectedObject2.ymin);

        float unn = square1 + square2 - intr;

        return static_cast<float>(intr) / unn;
    }

    DetectedObject scale(float scale_x, float scale_y) const {
        return DetectedObject(objectType, xmin * scale_x, ymin * scale_y, xmax * scale_x, ymax * scale_y, prob,
                              difficult);
    }
};

class ImageDescription {
public:
    const std::list<DetectedObject> alist;
    const bool check_probs;

    explicit ImageDescription(const std::list<DetectedObject> &_alist, bool _check_probs = false)
            : alist(_alist), check_probs(_check_probs) {
    }

    static float ioUMultiple(const ImageDescription &detectedObjects, const ImageDescription &desiredObjects) {
        const ImageDescription *detectedObjectsSmall, *detectedObjectsBig;
        bool check_probs = desiredObjects.check_probs;

        if (detectedObjects.alist.size() < desiredObjects.alist.size()) {
            detectedObjectsSmall = &detectedObjects;
            detectedObjectsBig = &desiredObjects;
        } else {
            detectedObjectsSmall = &desiredObjects;
            detectedObjectsBig = &detectedObjects;
        }

        std::list<DetectedObject> doS = detectedObjectsSmall->alist;
        std::list<DetectedObject> doB = detectedObjectsBig->alist;

        float fullScore = 0.0f;
        while (doS.size() > 0) {
            float score = 0.0f;
            std::list<DetectedObject>::iterator bestJ = doB.end();
            for (auto j = doB.begin(); j != doB.end(); j++) {
                float curscore = DetectedObject::ioU(*doS.begin(), *j);
                if (score < curscore) {
                    score = curscore;
                    bestJ = j;
                }
            }

            float coeff = 1.0;
            if (check_probs) {
                if (bestJ != doB.end()) {
                    float mn = std::min((*bestJ).prob, (*doS.begin()).prob);
                    float mx = std::max((*bestJ).prob, (*doS.begin()).prob);

                    coeff = mn / mx;
                }
            }

            doS.pop_front();
            if (bestJ != doB.end()) doB.erase(bestJ);
            fullScore += coeff * score;
        }
        fullScore /= detectedObjectsBig->alist.size();


        return fullScore;
    }

    ImageDescription scale(float scale_x, float scale_y) const {
        std::list<DetectedObject> slist;
        for (auto &dob : alist) {
            slist.push_back(dob.scale(scale_x, scale_y));
        }
        return ImageDescription(slist, check_probs);
    }
};

struct AveragePrecisionCalculator {
private:
    enum MatchKind {
        TruePositive, FalsePositive
    };

    /**
     * Here we count all TP and FP matches for all the classes in all the images.
     */
    std::map<int, std::vector<std::pair<double, MatchKind>>> matches;

    std::map<int, int> N;

    double threshold;

    static bool SortBBoxDescend(const DetectedObject &bbox1, const DetectedObject &bbox2) {
        return bbox1.prob > bbox2.prob;
    }

    static bool SortPairDescend(const std::pair<double, MatchKind> &p1, const std::pair<double, MatchKind> &p2) {
        return p1.first > p2.first;
    }

public:
    explicit AveragePrecisionCalculator(double _threshold) : threshold(_threshold) {}

    // gt_bboxes -> des
    // bboxes -> det

    void consumeImage(const ImageDescription &detectedObjects, const ImageDescription &desiredObjects) {
        // Collecting IoU values
        std::vector<bool> visited(desiredObjects.alist.size(), false);
        std::vector<DetectedObject> bboxes{std::begin(detectedObjects.alist), std::end(detectedObjects.alist)};
        std::sort(bboxes.begin(), bboxes.end(), SortBBoxDescend);


        for (auto &&detObj : bboxes) {
            // Searching for the best match to this detection
            // Searching for desired object
            float overlap_max = -1;
            int jmax = -1;
            auto desmax = desiredObjects.alist.end();

            int j = 0;
            for (auto desObj = desiredObjects.alist.begin(); desObj != desiredObjects.alist.end(); desObj++, j++) {
                double iou = DetectedObject::ioU(detObj, *desObj);
                if (iou > overlap_max) {
                    overlap_max = static_cast<float>(iou);
                    jmax = j;
                    desmax = desObj;
                }
            }

            MatchKind mk;
            if (overlap_max >= threshold) {
                if (!desmax->difficult) {
                    if (!visited[jmax]) {
                        mk = TruePositive;
                        visited[jmax] = true;
                    } else {
                        mk = FalsePositive;
                    }
                    matches[detObj.objectType].push_back(std::make_pair(detObj.prob, mk));
                }
            } else {
                mk = FalsePositive;
                matches[detObj.objectType].push_back(std::make_pair(detObj.prob, mk));
            }
        }

        for (auto desObj = desiredObjects.alist.begin(); desObj != desiredObjects.alist.end(); desObj++) {
            if (!desObj->difficult) {
                N[desObj->objectType]++;
            }
        }
    }

    std::map<int, double> calculateAveragePrecisionPerClass() const {
        /**
         * Precision-to-TP curve per class (a variation of precision-to-recall curve without dividing into N)
         */
        std::map<int, std::map<int, double>> precisionToTP;


        std::map<int, double> res;

        for (auto m : matches) {
            // Sorting
            std::sort(m.second.begin(), m.second.end(), SortPairDescend);

            int clazz = m.first;
            int TP = 0, FP = 0;

            std::vector<double> prec;
            std::vector<double> rec;

            for (auto mm : m.second) {
                // Here we are descending in a probability value
                MatchKind mk = mm.second;
                if (mk == TruePositive) TP++;
                else if (mk == FalsePositive) FP++;

                double precision = static_cast<double>(TP) / (TP + FP);
                double recall = 0;
                if (N.find(clazz) != N.end()) {
                    recall = static_cast<double>(TP) / N.at(clazz);
                }

                prec.push_back(precision);
                rec.push_back(recall);
            }

            int num = rec.size();

            // 11point from Caffe
            double ap = 0;
            std::vector<float> max_precs(11, 0.);
            int start_idx = num - 1;
            for (int j = 10; j >= 0; --j) {
                for (int i = start_idx; i >= 0; --i) {
                    if (rec[i] < j / 10.) {
                        start_idx = i;
                        if (j > 0) {
                            max_precs[j - 1] = max_precs[j];
                        }
                        break;
                    } else {
                        if (max_precs[j] < prec[i]) {
                            max_precs[j] = static_cast<float>(prec[i]);
                        }
                    }
                }
            }
            for (int j = 10; j >= 0; --j) {
                ap += max_precs[j] / 11;
            }
            res[clazz] = ap;
        }

        return res;
    }
};

/**
* @brief Adds colored rectangles to the image
* @param data - data where rectangles are put
* @param height - height of the rectangle
* @param width - width of the rectangle
* @param detectedObjects - vector of detected objects
*/
static UNUSED void
addRectangles(unsigned char *data, size_t height, size_t width, std::vector<DetectedObject> detectedObjects) {
    std::vector<Color> colors = {
            {128, 64,  128},
            {232, 35,  244},
            {70,  70,  70},
            {156, 102, 102},
            {153, 153, 190},
            {153, 153, 153},
            {30,  170, 250},
            {0,   220, 220},
            {35,  142, 107},
            {152, 251, 152},
            {180, 130, 70},
            {60,  20,  220},
            {0,   0,   255},
            {142, 0,   0},
            {70,  0,   0},
            {100, 60,  0},
            {90,  0,   0},
            {230, 0,   0},
            {32,  11,  119},
            {0,   74,  111},
            {81,  0,   81}
    };

    for (size_t i = 0; i < detectedObjects.size(); i++) {
        int cls = detectedObjects[i].objectType % colors.size();

        int xmin = static_cast<int>(detectedObjects[i].xmin * width);
        int xmax = static_cast<int>(detectedObjects[i].xmax * width);
        int ymin = static_cast<int>(detectedObjects[i].ymin * height);
        int ymax = static_cast<int>(detectedObjects[i].ymax * height);

        size_t shift_first = ymin * width * 3;
        size_t shift_second = ymax * width * 3;
        for (int x = xmin; x < xmax; x++) {
            data[shift_first + x * 3] = colors.at(cls).red();
            data[shift_first + x * 3 + 1] = colors.at(cls).green();
            data[shift_first + x * 3 + 2] = colors.at(cls).blue();
            data[shift_second + x * 3] = colors.at(cls).red();
            data[shift_second + x * 3 + 1] = colors.at(cls).green();
            data[shift_second + x * 3 + 2] = colors.at(cls).blue();
        }

        shift_first = xmin * 3;
        shift_second = xmax * 3;
        for (int y = ymin; y < ymax; y++) {
            data[shift_first + y * width * 3] = colors.at(cls).red();
            data[shift_first + y * width * 3 + 1] = colors.at(cls).green();
            data[shift_first + y * width * 3 + 2] = colors.at(cls).blue();
            data[shift_second + y * width * 3] = colors.at(cls).red();
            data[shift_second + y * width * 3 + 1] = colors.at(cls).green();
            data[shift_second + y * width * 3 + 2] = colors.at(cls).blue();
        }
    }
}

inline std::size_t getTensorWidth(const InferenceEngine::TensorDesc &desc) {
    const auto &layout = desc.getLayout();
    const auto &dims = desc.getDims();
    const auto &size = dims.size();
    if ((size >= 2) &&
        (layout == InferenceEngine::Layout::NCHW ||
         layout == InferenceEngine::Layout::NHWC ||
         layout == InferenceEngine::Layout::NCDHW ||
         layout == InferenceEngine::Layout::NDHWC ||
         layout == InferenceEngine::Layout::OIHW ||
         layout == InferenceEngine::Layout::CHW ||
         layout == InferenceEngine::Layout::HW)) {
        // Regardless of layout, dimensions are stored in fixed order
        return dims.back();
    } else {
        THROW_IE_EXCEPTION << "Tensor does not have width dimension";
    }
    return 0;
}

inline std::size_t getTensorHeight(const InferenceEngine::TensorDesc &desc) {
    const auto &layout = desc.getLayout();
    const auto &dims = desc.getDims();
    const auto &size = dims.size();
    if ((size >= 2) &&
        (layout == InferenceEngine::Layout::NCHW ||
         layout == InferenceEngine::Layout::NHWC ||
         layout == InferenceEngine::Layout::NCDHW ||
         layout == InferenceEngine::Layout::NDHWC ||
         layout == InferenceEngine::Layout::OIHW ||
         layout == InferenceEngine::Layout::CHW ||
         layout == InferenceEngine::Layout::HW)) {
        // Regardless of layout, dimensions are stored in fixed order
        return dims.at(size - 2);
    } else {
        THROW_IE_EXCEPTION << "Tensor does not have height dimension";
    }
    return 0;
}

inline std::size_t getTensorChannels(const InferenceEngine::TensorDesc &desc) {
    const auto &layout = desc.getLayout();
    if (layout == InferenceEngine::Layout::NCHW ||
        layout == InferenceEngine::Layout::NHWC ||
        layout == InferenceEngine::Layout::NCDHW ||
        layout == InferenceEngine::Layout::NDHWC ||
        layout == InferenceEngine::Layout::C ||
        layout == InferenceEngine::Layout::CHW ||
        layout == InferenceEngine::Layout::NC ||
        layout == InferenceEngine::Layout::CN) {
        // Regardless of layout, dimensions are stored in fixed order
        const auto &dims = desc.getDims();
        switch (desc.getLayoutByDims(dims)) {
            case InferenceEngine::Layout::C:
                return dims.at(0);
            case InferenceEngine::Layout::NC:
                return dims.at(1);
            case InferenceEngine::Layout::CHW:
                return dims.at(0);
            case InferenceEngine::Layout::NCHW:
                return dims.at(1);
            case InferenceEngine::Layout::NCDHW:
                return dims.at(1);
            case InferenceEngine::Layout::SCALAR:   // [[fallthrough]]
            case InferenceEngine::Layout::BLOCKED:  // [[fallthrough]]
            default:
                THROW_IE_EXCEPTION << "Tensor does not have channels dimension";
        }
    } else {
        THROW_IE_EXCEPTION << "Tensor does not have channels dimension";
    }
    return 0;
}

inline std::size_t getTensorBatch(const InferenceEngine::TensorDesc &desc) {
    const auto &layout = desc.getLayout();
    if (layout == InferenceEngine::Layout::NCHW ||
        layout == InferenceEngine::Layout::NHWC ||
        layout == InferenceEngine::Layout::NCDHW ||
        layout == InferenceEngine::Layout::NDHWC ||
        layout == InferenceEngine::Layout::NC ||
        layout == InferenceEngine::Layout::CN) {
        // Regardless of layout, dimensions are stored in fixed order
        const auto &dims = desc.getDims();
        switch (desc.getLayoutByDims(dims)) {
            case InferenceEngine::Layout::NC:
                return dims.at(0);
            case InferenceEngine::Layout::NCHW:
                return dims.at(0);
            case InferenceEngine::Layout::NCDHW:
                return dims.at(0);
            case InferenceEngine::Layout::CHW:      // [[fallthrough]]
            case InferenceEngine::Layout::C:        // [[fallthrough]]
            case InferenceEngine::Layout::SCALAR:   // [[fallthrough]]
            case InferenceEngine::Layout::BLOCKED:  // [[fallthrough]]
            default:
                THROW_IE_EXCEPTION << "Tensor does not have channels dimension";
        }
    } else {
        THROW_IE_EXCEPTION << "Tensor does not have channels dimension";
    }
    return 0;
}

inline void showAvailableDevices() {
    InferenceEngine::Core ie;
    std::vector<std::string> devices = ie.GetAvailableDevices();

    std::cout << std::endl;
    std::cout << "Available target devices:";
    for (const auto &device : devices) {
        std::cout << "  " << device;
    }
    std::cout << std::endl;

}

#endif